Download Research on The Conceptual Framework of Spatio

Research on The Framework of Spatio-Temporal
Data Warehouse
WANG Jizhou, LI Chengming
Institute of GIS, Chinese Academy of Surveying and Mapping
No.16, Road Beitaiping, District Haidian, Beijing, P.R.China, 100039
[email protected] [email protected]
Abstract: Data Warehouse is “the topic-oriented, integrated, static datasets of various periods which
are used to support decision-making in administration”. Driven by the requirement of mass
spatio-temporal data management and application, Spatio-Temporal Data Warehouse was put forward,
and many researchers distributed all over the world focused their energy on it. Although the research on
Spatio-Temporal Data Warehouse is going deep, there are still many key problems to be solved, such as
the design principle, system framework, spatio-temporal data model, spatio-temporal data process,
spatial data mining and etc. In this paper, we discuss the concept of Spatio-Temporal Data Warehouse,
and analyze the organization model of spatio-temporal data. Based on the above, we found the
framework of Spatio-Temporal Data Warehouse, which is composed of data layer, management layer
and application layer. The functions of Spatio-Temporal Data Warehouse should include data analysis
besides data process and data storage. When users apply certain kind of data services, Spatio-Temporal
Data Warehouse locates the right data by metadata management system, then start data processing tools
to form data products or found multidimensional data cube which serves the data mining and OLAP.
All kinds of distributed databases make up data sources of Spatio-Temporal Data Warehouse, including
DEM, DRG, DLG, DOM, Place Name and other databases in existence. The management layer
implements heterogeneous data processing, metadata management and spatio-temporal data storage.
The application layer provides data products service, multidimensional data cube, data mining tools
and on-line analytical process.
Keywords: Data Warehouse, Spatio-Temporal Data Warehouse, Spatio-Temporal Data Model, OLAP
1.Introduction
W.H.Inmon defined Data Warehouse as “the topic-oriented, integrated, static datasets of various
periods which are used to support decision-making in administration”. Based on the Data Warehouse
technology, we found Spatio-Temporal Data Warehouse by importing temporal and spatial data into
Data Warehouse. Utilizing this warehouse, we abstract information according to application topics from
various GIS, spatial databases and historical databases which are in different spatio-temporal scale, and
supply spatio-temporal information by doing data processes to serve the scientific research, region
economic decision-making, resource policy-constituting and so on.
Driven by the requirement of mass spatio-temporal data management and the progress of Data
Warehouse technology, Spatio-Temporal Data Warehouse came into the world. In recent years, many
researchers distributed all over the world focused their energy on it, and acquired many achievements.
(Maryvonne, 2002; ZHAO,2000; LI,1999;Dimitris,2001;Giuseppe, 2001). Although the research on
Spatio-Temporal Data Warehouse is going deep, there are still many key problems to be solved, such as
the design principle, system framework, spatio-temporal data model, spatio-temporal data process,
spatial data mining and etc. In this paper, we discuss the concept of Spatio-Temporal Data Warehouse
on the basis of an brief introduction of Data Warehouse, and analyze the organization model of
spatio-temporal data. Based on the above, we found the framework of Spatio-Temporal Data
Warehouse, which is composed of data sources, management system and application tools.
2. The organization of spatio-temporal data
2.1. Spatio-temporal data model
Two concepts of time are involved in research on temporal characteristic of geographic entities,
which are world time and system time. The former means the time when an entity change take place in
reality, and the latter means the time that records the entity change in database. In general, we only take
the system time into account in GIS(Raafat, et al,1994). In Spatio-Temporal Data Warehouse, we also
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use the system time to mark the historical changes of entity. There are three methods to describe the
changes(Ariav,1986;GONG,1997). When one or more objects change in a event, the first method
records it by creating a new edition of all the tables related to these objects, the second by creating a
new edition of the changed objects, and the third method records these changes by only adding a new
record of the changed objects attribute field to the related table. By comparing theses methods, we can
draw the conclusion that the first has the most redundancy, the second has edition controlling problems,
and the third is ideal because it not only has the least redundancy, but also is easy to do querying and
analyzing operations for its having historical information in the same record. Research on the
spatio-temporal data model have made great progress in recent years. For examples, GONG Jian-ya put
forward object-oriented spatio-temporal data model by enhancing the third method(GONG,1997),
Giorgos Mountrakis founded a differential spatio-temporal model(Giorgos, et al,2002).
2.2 Storage of spatio-temporal data
The purpose of applying Spatio-Temporal Data Warehouse is to make the mass data sharing in the
broadest scope. Since users have various data demands, they have various requests towards
Spatio-Temporal Data Warehouse. To meet most users’ demands and achieve fast responds, we use
multi-layer strategy to store spatio-temporal information. Commonly, there are three layers: Data
Market, Department Warehouse and Whole Warehouse(LI, et al,1999). Data Market is outcome
datasets of querying in a lower level, and it mainly satisfy demands of general users. Department
Warehouse which is based on the department topic can meet the demands of department leaders. And
Whole Warehouse is founded for high-class decision.
3. The framework of Spatio-Temporal Data Warehouse
The functions of Spatio-Temporal Data Warehouse should include data analysis besides data process
and data storage. When users apply certain kind of data services, Spatio-Temporal Data Warehouse
locates the right data by metadata management system, then start data processing tools to form data
products or found multidimensional data cube which serves the data mining and OLAP. Thus we think
that the framework of Spatio-Temporal Data Warehouse is composed of Data Sources, Management
System and Application Tools. Figure 1 shows the framework of Spatio-Temporal Data Warehouse in
detail.
3.1. Data sources
3.1.1. Distributed spatio-temporal databases
These databases are the information source of Spatio-Temporal Data Warehouse, which include all
kinds of DEM, DRG, DLG, DOM, Place Name and other databases in existence. Not only the hardware
and software platforms that these databases run on are diverse, but also the encoding specifications,
projection systems, data formats and etc of them are in variety.
3.1.2. Special databases
Special databases store the inner data of special departments which are used to fulfill OLAP and
SOLAP by associating with the spatio-temporal data, such as the populace data of police, statistical
data of revenue, etc.
3.2. Management system
3.2.1. Data processing
Because the databases in existence serve various applications, there are many differences between
each other in data capture methods, encoding specifications, projection systems, data classification
standards and data formats, and there are even data mistakes in some databases. Hence we must do
some data processing before putting them into Spatio-Temporal Data Warehouse, which includes data
conversion, spatial transformation and data filter (ZHAO, et al,2000). Data conversion means unifying
the data encoding and structure, adding the temporal mark, operation and semantic conversion of data
sets. Spatial transformation means unifying the data coordinate and scale. Data filter means the data
extraction, mainly including recomposing of data fields, deleting the useless information, translating
and decoding of fields, supplement of the devoid information and verification of data integrity.
Spatio-Temporal Data Warehouse should supply data products for various users. Since there are so
many users who have diversiform demands, we have to do different processing on the data towards
different demands. The user-oriented data processing mainly includes data integration, data fusion and
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data recomposition. Data integration means the overlay of multiple data that all the data remain their
own characteristics after overlaying, for an example, using DOM and digital map to produce image
map. Unlike data integration, data fusion may create a new kind of data, such as the
pseudocolor-composite image. Data recomposition means the organization of various geographic
features, for an instance, the city frame data for the department of estate management which is
composed of boundary, street and water system.
Figure 1 The Framework of Spatio-Temporal Data Warehouse
3.2.2. Metadata management system
Metadata is the background information that describes the content, quality, condition and other
appropriate characteristics of the data. Metadata is a simple mechanism to inform others of the
existence of datasets, their purpose and scope. Key developments in spatial metadata standards are the
ISO STANDARD 15046-15 METADATA, the Federal Geographic Data Committee’s content standard
for Digital Geospatial Metadata FGDC, CSGDM, the European organization responsible for standards
CEN/TC 287. According to these standards, spatial metadata should include eleven categories
information, which are identification information, data quality information, maintenance information,
spatial representation information, reference system information, entity and attribute information,
distribution information, metadata reference information, citation information, time period information
and contact information.
3.2.3. Spatio-Temporal Data Warehouse
The data in existence will be stored in the Spatio-Temporal Data Warehouse after processing. In the
warehouse, we organize and manage the mass data using multiple dimension mechanism, which
includes temporal dimension(one dimension), spatial dimension(three dimensions, X, Y and Z) and
attribute dimension(multiple dimensions, name, type, address, etc.). And each dimension has various
granularities, such as temporal dimension may be partitioned into year, month, day, hour, minute and
second, spatial dimension may be divided into nation, province, city, county and town. So users can
utilize the spatio-temporal data from diversiform points.
3.3. Application tools
3.3.1. Data products service
Spatio-Temporal Data Warehouse can supply various data products service through data integration,
data fusion and data recomposition, such as the on-line query, scan and display, the on-line order and
distribution of data products and so on.
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3.3.2. Multidimensional data cube
Because the Spatio-Temporal data warehouse uses multiple dimension mechanism in the
organization and management, it can expediently found the multidimensional data cube, or super cube
data model, which serves the data mining and OLAP. The cube is composed of temporal, spatial and
attribute dimension, whose actual dimension number depends on the decided demand. For an example,
when taking on the analysis of populace movement, we can build an data cube which is composed of
five dimensions including temporal, X, Y, Z and populace statistical data.
3.3.3. Data mining
Data mining is a new technology that supports decision by abstracting knowledge from databases.
All over the world there are many researchers who devote themselves to it. Professor Han Jiawei in
Canada has implemented an prototype system based on MapInfo, which has many analysis methods of
data mining including spatial comparison, spatial correlation, spatial clustering, spatial classification
and so on. Professor Li Deren in China put forward that people could extract knowledge as spatial
information, spatial relation, attribute relation and etc from GIS databases. We think that the
spatio-temporal data comprises of three kinds of elements: temporal, spatial and attribute information,
thus the spatial data mining can be partitioned into four kinds: temporal field data mining for the
simulation and retrieval of change processes, the spatial field mining for geographical distribution of
topic information, the attribute field mining of spatial correlation and clustering for region economic
decision and the multi-fields associated mining for governmental decision.
3.3.4. OLAP and SOLAP
OLAP(On-Line Analytical Process) technology enables users to quickly analyze mass data. OLAP
systems are generally based on a three-tiers architecture including a data warehouse with integrated
data, an OLAP server for the dimensional view and an OLAP client, i.e. a user interface for the rapid
and easy exploration of data(Han, et al,2001). SOLAP(Spatial On-Line Analytical Process) systems are
built to support the spatio-temporal analysis as well as the exploration of data according to a
multidimensional approach of data warehouse. Sometimes we should associate the spatio-temporal data
with the special data by the unique key word in OLAP and SOLAP, which is could be the coordinate,
place name, project number and so on in both the spatio-temporal data and the special data. By
applying the analysis technology as rotation, drilling, nesting, slicing and visualizing on the
multidimensional data cube, OLAP and SOLAP enable users to look into the spatio-temporal data from
various sides and discover the potential relations of data, and at last facilitate the decision-making.
4. Conclusion
Since the spatio-temporal data is so complicated and dynamic, the existing data warehouse
technology can’t solve all the matters of Spatio-Temporal Data Warehouse. The research on
Spatio-Temporal Data Warehouse is still in elementary stage. In this paper, we found the framework of
Spatio-Temporal Data Warehouse from three tiers: data, management and application, which covers the
whole process from building warehouse to supplying services. Next we will go on to take deeper study
on Spatio-Temporal Data Warehouse technology which includes the data processing, metadata
management, data storage, data mining and so on, and perfect the framework in practices.
References
[1]
[2]
W.H.Inmon,1992. Building The Data Warehouse. Canada: John Wiley & Sons Inc.
ZHAO Pei-sheng, YANG Chong-jun,2000,The Technology and application of spatial data warehouse.
Journal of Remote Sensing, 2000,4(2) (in Chinese).
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